PyCaret: 100 Tips for Streamlined Machine Learning Workflows

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PyCaret is a Python library for automated machine learning. Here are 100 tips for working with PyCaret:

1. Basic Setup:

1. Install PyCaret with pip install pycaret.
2. Import PyCaret with from pycaret.classification import * or from pycaret.regression import *.
3. Initialize the PyCaret environment with exp1 = setup(data, target='target_column').
4. View the available pre-processing options with get_config('X_train').

2. Data Exploration:

1. Get a summary of the dataset with compare_models().
2. Visualize the data distribution with create_model('distribution').
3. Generate an interactive correlation plot with create_model('correlation').
4. Explore feature importance with create_model('feature').
5. Evaluate missing values with missing_diagrams().

3. Model Training:

1. Train a baseline model with compare_models() and choose the best-performing one.
2. Create an ensemble model with ensemble_model().
3. Use bagging with create_model('dt', ensemble=True, method='bagging').